Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Adapted vocabularies for generic visual categorization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Visual cue cluster construction via information bottleneck principle and kernel density estimation
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Image classification using spatial pyramid coding and visual word reweighting
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Supervised visual vocabulary with category information
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
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Recently, the bag of visual words based image representation is getting popular in object category recognition. Since the codebook of the bag-of-words (BOW) based image representation approach is typically constructed by only measuring the visual similarity of local image features (e.g., k-means), the resulting codebooks may not capture the desired information for object category recognition. This paper proposes a novel optimization method for discriminative codebook construction that considers the category information of local image features as an additional term in traditional visual-similarity-only based codebook construction methods. The category sensitive codebook is constructed through solving an optimization problem. Therefore, the category sensitive codebook construction method goes one step beyond visual-similarity-only methods. Besides, the proposed category sensitive codebook construction method can be implemented with k-means clustering very efficiently and effectively. Experimental results on PASCAL VOC Challenge 2006 data set demonstrate the effectiveness of our method.